diff --git a/deepspeech/training/gradclip.py b/deepspeech/training/gradclip.py
index d0f9803d2664f697e58ea4ed2087d5c44526e1f9..f46814eb0ae04887ceb4c7c1f674fc360f3644c0 100644
--- a/deepspeech/training/gradclip.py
+++ b/deepspeech/training/gradclip.py
@@ -27,6 +27,9 @@ class ClipGradByGlobalNormWithLog(paddle.nn.ClipGradByGlobalNorm):
     def __init__(self, clip_norm):
         super().__init__(clip_norm)
 
+    def __repr__(self):
+        return f"{self.__class__.__name__}(global_clip_norm={self.clip_norm})"
+
     @imperative_base.no_grad
     def _dygraph_clip(self, params_grads):
         params_and_grads = []
diff --git a/deepspeech/training/optimizer.py b/deepspeech/training/optimizer.py
index 2e62a7ed71da8b048538d111c9ed1f5144baf131..f7933f8d4f986ea87302330691d6078f2c51c0de 100644
--- a/deepspeech/training/optimizer.py
+++ b/deepspeech/training/optimizer.py
@@ -20,7 +20,7 @@ from paddle.regularizer import L2Decay
 
 from deepspeech.training.gradclip import ClipGradByGlobalNormWithLog
 from deepspeech.utils.dynamic_import import dynamic_import
-from deepspeech.utils.dynamic_import import filter_valid_args
+from deepspeech.utils.dynamic_import import instance_class
 from deepspeech.utils.log import Log
 
 __all__ = ["OptimizerFactory"]
@@ -80,5 +80,4 @@ class OptimizerFactory():
 
         args.update({"grad_clip": grad_clip, "weight_decay": weight_decay})
 
-        args = filter_valid_args(args)
-        return module_class(**args)
+        return instance_class(module_class, args)
diff --git a/deepspeech/utils/dynamic_import.py b/deepspeech/utils/dynamic_import.py
index 41978bc93f97bbcd34f4719ecefb964900a36c0a..533f15eeefdae8b7d13b8d8742cb5cbddc7696db 100644
--- a/deepspeech/utils/dynamic_import.py
+++ b/deepspeech/utils/dynamic_import.py
@@ -12,15 +12,18 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import importlib
+import inspect
 from typing import Any
 from typing import Dict
+from typing import List
 from typing import Text
 
 from deepspeech.utils.log import Log
+from deepspeech.utils.tensor_utils import has_tensor
 
 logger = Log(__name__).getlog()
 
-__all__ = ["dynamic_import", "instance_class", "filter_valid_args"]
+__all__ = ["dynamic_import", "instance_class"]
 
 
 def dynamic_import(import_path, alias=dict()):
@@ -43,14 +46,22 @@ def dynamic_import(import_path, alias=dict()):
     return getattr(m, objname)
 
 
-def filter_valid_args(args: Dict[Text, Any]):
-    # filter out `val` which is None
-    new_args = {key: val for key, val in args.items() if val is not None}
+def filter_valid_args(args: Dict[Text, Any], valid_keys: List[Text]):
+    # filter by `valid_keys` and filter `val` is not None
+    new_args = {
+        key: val
+        for key, val in args.items() if (key in valid_keys and val is not None)
+    }
     return new_args
 
 
+def filter_out_tenosr(args: Dict[Text, Any]):
+    return {key: val for key, val in args.items() if not has_tensor(val)}
+
+
 def instance_class(module_class, args: Dict[Text, Any]):
-    # filter out `val` which is None
-    new_args = filter_valid_args(args)
-    logger.info(f"Instance: {module_class.__name__} {new_args}.")
+    valid_keys = inspect.signature(module_class).parameters.keys()
+    new_args = filter_valid_args(args, valid_keys)
+    logger.info(
+        f"Instance: {module_class.__name__} {filter_out_tenosr(new_args)}.")
     return module_class(**new_args)
diff --git a/deepspeech/utils/tensor_utils.py b/deepspeech/utils/tensor_utils.py
index 7679d9e1c573a552eaf0086da12b19dd018c9024..9bff6b0f3f35ccb7a5392481efd2da7fb9e70ea1 100644
--- a/deepspeech/utils/tensor_utils.py
+++ b/deepspeech/utils/tensor_utils.py
@@ -19,11 +19,25 @@ import paddle
 
 from deepspeech.utils.log import Log
 
-__all__ = ["pad_sequence", "add_sos_eos", "th_accuracy"]
+__all__ = ["pad_sequence", "add_sos_eos", "th_accuracy", "has_tensor"]
 
 logger = Log(__name__).getlog()
 
 
+def has_tensor(val):
+    if isinstance(val, (list, tuple)):
+        for item in val:
+            if has_tensor(item):
+                return True
+    elif isinstance(val, dict):
+        for k, v in val.items():
+            print(k)
+            if has_tensor(v):
+                return True
+    else:
+        return paddle.is_tensor(val)
+
+
 def pad_sequence(sequences: List[paddle.Tensor],
                  batch_first: bool=False,
                  padding_value: float=0.0) -> paddle.Tensor: